Overview

Dataset statistics

Number of variables50
Number of observations229
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory89.6 KiB
Average record size in memory400.6 B

Variable types

Categorical35
Numeric15

Alerts

Net Official Development Assist. received (% of GNI) has constant value "-99" Constant
country has a high cardinality: 229 distinct values High cardinality
Surface area (km2) has a high cardinality: 225 distinct values High cardinality
GDP growth rate (annual %, const. 2005 prices) has a high cardinality: 91 distinct values High cardinality
Economy: Agriculture (% of GVA) has a high cardinality: 139 distinct values High cardinality
Employment: Agriculture (% of employed) has a high cardinality: 160 distinct values High cardinality
Employment: Industry (% of employed) has a high cardinality: 139 distinct values High cardinality
Employment: Services (% of employed) has a high cardinality: 165 distinct values High cardinality
Unemployment (% of labour force) has a high cardinality: 129 distinct values High cardinality
Labour force participation (female/male pop. %) has a high cardinality: 202 distinct values High cardinality
International trade: Exports (million US$) has a high cardinality: 206 distinct values High cardinality
International trade: Imports (million US$) has a high cardinality: 210 distinct values High cardinality
International trade: Balance (million US$) has a high cardinality: 211 distinct values High cardinality
Balance of payments, current account (million US$) has a high cardinality: 178 distinct values High cardinality
Population growth rate (average annual %) has a high cardinality: 58 distinct values High cardinality
Urban population growth rate (average annual %) has a high cardinality: 64 distinct values High cardinality
Fertility rate, total (live births per woman) has a high cardinality: 51 distinct values High cardinality
Life expectancy at birth (females/males, years) has a high cardinality: 211 distinct values High cardinality
Population age distribution (0-14 / 60+ years, %) has a high cardinality: 221 distinct values High cardinality
International migrant stock (000/% of total pop.) has a high cardinality: 229 distinct values High cardinality
Refugees and others of concern to UNHCR (in thousands) has a high cardinality: 140 distinct values High cardinality
Infant mortality rate (per 1000 live births has a high cardinality: 157 distinct values High cardinality
Education: Government expenditure (% of GDP) has a high cardinality: 63 distinct values High cardinality
Education: Primary gross enrol. ratio (f/m per 100 pop.) has a high cardinality: 187 distinct values High cardinality
Education: Secondary gross enrol. ratio (f/m per 100 pop.) has a high cardinality: 176 distinct values High cardinality
Education: Tertiary gross enrol. ratio (f/m per 100 pop.) has a high cardinality: 160 distinct values High cardinality
Mobile-cellular subscriptions (per 100 inhabitants) has a high cardinality: 196 distinct values High cardinality
Mobile-cellular subscriptions (per 100 inhabitants).1 has a high cardinality: 195 distinct values High cardinality
Threatened species (number) has a high cardinality: 194 distinct values High cardinality
Forested area (% of land area) has a high cardinality: 214 distinct values High cardinality
Energy supply per capita (Gigajoules) has a high cardinality: 163 distinct values High cardinality
Pop. using improved drinking water (urban/rural, %) has a high cardinality: 186 distinct values High cardinality
Pop. using improved sanitation facilities (urban/rural, %) has a high cardinality: 120 distinct values High cardinality
Population in thousands (2017) is highly correlated with GDP: Gross domestic product (million current US$) and 3 other fieldsHigh correlation
GDP: Gross domestic product (million current US$) is highly correlated with Population in thousands (2017) and 3 other fieldsHigh correlation
GDP per capita (current US$) is highly correlated with Economy: Services and other activity (% of GVA) and 2 other fieldsHigh correlation
Economy: Industry (% of GVA) is highly correlated with Population in thousands (2017) and 2 other fieldsHigh correlation
Economy: Services and other activity (% of GVA) is highly correlated with GDP per capita (current US$)High correlation
Agricultural production index (2004-2006=100) is highly correlated with Food production index (2004-2006=100)High correlation
Food production index (2004-2006=100) is highly correlated with Agricultural production index (2004-2006=100)High correlation
Urban population (% of total population) is highly correlated with GDP per capita (current US$) and 1 other fieldsHigh correlation
Health: Total expenditure (% of GDP) is highly correlated with Seats held by women in national parliaments %High correlation
Seats held by women in national parliaments % is highly correlated with GDP: Gross domestic product (million current US$) and 2 other fieldsHigh correlation
Individuals using the Internet (per 100 inhabitants) is highly correlated with Population in thousands (2017)High correlation
CO2 emission estimates (million tons/tons per capita) is highly correlated with Population in thousands (2017) and 3 other fieldsHigh correlation
Energy production, primary (Petajoules) is highly correlated with GDP per capita (current US$) and 1 other fieldsHigh correlation
Population in thousands (2017) is highly correlated with GDP: Gross domestic product (million current US$) and 1 other fieldsHigh correlation
GDP: Gross domestic product (million current US$) is highly correlated with Population in thousands (2017) and 1 other fieldsHigh correlation
Economy: Industry (% of GVA) is highly correlated with Economy: Services and other activity (% of GVA) and 2 other fieldsHigh correlation
Economy: Services and other activity (% of GVA) is highly correlated with Economy: Industry (% of GVA) and 2 other fieldsHigh correlation
Agricultural production index (2004-2006=100) is highly correlated with Food production index (2004-2006=100)High correlation
Food production index (2004-2006=100) is highly correlated with Agricultural production index (2004-2006=100)High correlation
Health: Total expenditure (% of GDP) is highly correlated with Economy: Industry (% of GVA) and 2 other fieldsHigh correlation
Seats held by women in national parliaments % is highly correlated with Economy: Industry (% of GVA) and 2 other fieldsHigh correlation
CO2 emission estimates (million tons/tons per capita) is highly correlated with Population in thousands (2017) and 1 other fieldsHigh correlation
Population in thousands (2017) is highly correlated with GDP: Gross domestic product (million current US$) and 1 other fieldsHigh correlation
GDP: Gross domestic product (million current US$) is highly correlated with Population in thousands (2017) and 1 other fieldsHigh correlation
GDP per capita (current US$) is highly correlated with Economy: Services and other activity (% of GVA) and 1 other fieldsHigh correlation
Economy: Services and other activity (% of GVA) is highly correlated with GDP per capita (current US$)High correlation
Agricultural production index (2004-2006=100) is highly correlated with Food production index (2004-2006=100)High correlation
Food production index (2004-2006=100) is highly correlated with Agricultural production index (2004-2006=100)High correlation
CO2 emission estimates (million tons/tons per capita) is highly correlated with Population in thousands (2017) and 1 other fieldsHigh correlation
Energy production, primary (Petajoules) is highly correlated with GDP per capita (current US$)High correlation
Health: Physicians (per 1000 pop.) is highly correlated with Net Official Development Assist. received (% of GNI)High correlation
Education: Government expenditure (% of GDP) is highly correlated with Net Official Development Assist. received (% of GNI)High correlation
Net Official Development Assist. received (% of GNI) is highly correlated with Health: Physicians (per 1000 pop.) and 6 other fieldsHigh correlation
Fertility rate, total (live births per woman) is highly correlated with Net Official Development Assist. received (% of GNI)High correlation
GDP growth rate (annual %, const. 2005 prices) is highly correlated with Net Official Development Assist. received (% of GNI)High correlation
Urban population growth rate (average annual %) is highly correlated with Net Official Development Assist. received (% of GNI)High correlation
Region is highly correlated with Net Official Development Assist. received (% of GNI)High correlation
Population growth rate (average annual %) is highly correlated with Net Official Development Assist. received (% of GNI)High correlation
Region is highly correlated with GDP per capita (current US$) and 9 other fieldsHigh correlation
Population in thousands (2017) is highly correlated with GDP: Gross domestic product (million current US$) and 4 other fieldsHigh correlation
Population density (per km2, 2017) is highly correlated with GDP per capita (current US$) and 1 other fieldsHigh correlation
Sex ratio (m per 100 f, 2017) is highly correlated with Population growth rate (average annual %) and 3 other fieldsHigh correlation
GDP: Gross domestic product (million current US$) is highly correlated with Population in thousands (2017) and 3 other fieldsHigh correlation
GDP growth rate (annual %, const. 2005 prices) is highly correlated with Economy: Industry (% of GVA) and 8 other fieldsHigh correlation
GDP per capita (current US$) is highly correlated with Region and 4 other fieldsHigh correlation
Economy: Industry (% of GVA) is highly correlated with Region and 6 other fieldsHigh correlation
Economy: Services and other activity (% of GVA) is highly correlated with Region and 6 other fieldsHigh correlation
Agricultural production index (2004-2006=100) is highly correlated with Food production index (2004-2006=100) and 1 other fieldsHigh correlation
Food production index (2004-2006=100) is highly correlated with Agricultural production index (2004-2006=100) and 1 other fieldsHigh correlation
Population growth rate (average annual %) is highly correlated with Region and 7 other fieldsHigh correlation
Urban population (% of total population) is highly correlated with Region and 4 other fieldsHigh correlation
Urban population growth rate (average annual %) is highly correlated with Region and 8 other fieldsHigh correlation
Fertility rate, total (live births per woman) is highly correlated with Region and 8 other fieldsHigh correlation
Health: Total expenditure (% of GDP) is highly correlated with Region and 7 other fieldsHigh correlation
Health: Physicians (per 1000 pop.) is highly correlated with Region and 13 other fieldsHigh correlation
Education: Government expenditure (% of GDP) is highly correlated with Population in thousands (2017) and 7 other fieldsHigh correlation
Seats held by women in national parliaments % is highly correlated with Region and 6 other fieldsHigh correlation
Individuals using the Internet (per 100 inhabitants) is highly correlated with Population in thousands (2017) and 5 other fieldsHigh correlation
CO2 emission estimates (million tons/tons per capita) is highly correlated with Population in thousands (2017) and 4 other fieldsHigh correlation
Energy production, primary (Petajoules) is highly correlated with Sex ratio (m per 100 f, 2017) and 5 other fieldsHigh correlation
country is uniformly distributed Uniform
Surface area (km2) is uniformly distributed Uniform
Life expectancy at birth (females/males, years) is uniformly distributed Uniform
Population age distribution (0-14 / 60+ years, %) is uniformly distributed Uniform
International migrant stock (000/% of total pop.) is uniformly distributed Uniform
Threatened species (number) is uniformly distributed Uniform
Forested area (% of land area) is uniformly distributed Uniform
country has unique values Unique
International migrant stock (000/% of total pop.) has unique values Unique
Seats held by women in national parliaments % has 4 (1.7%) zeros Zeros
CO2 emission estimates (million tons/tons per capita) has 23 (10.0%) zeros Zeros

Reproduction

Analysis started2021-10-31 03:10:21.497511
Analysis finished2021-10-31 03:11:22.043280
Duration1 minute and 0.55 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

country
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct229
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
Guatemala
 
1
Guinea-Bissau
 
1
Uganda
 
1
Democratic Republic of the Congo
 
1
Ecuador
 
1
Other values (224)
224 

Length

Max length41
Median length8
Mean length10.59388646
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique229 ?
Unique (%)100.0%

Sample

1st rowAfghanistan
2nd rowAlbania
3rd rowAlgeria
4th rowAmerican Samoa
5th rowAndorra

Common Values

ValueCountFrequency (%)
Guatemala1
 
0.4%
Guinea-Bissau1
 
0.4%
Uganda1
 
0.4%
Democratic Republic of the Congo1
 
0.4%
Ecuador1
 
0.4%
Congo1
 
0.4%
Fiji1
 
0.4%
Uruguay1
 
0.4%
Brazil1
 
0.4%
Tokelau1
 
0.4%
Other values (219)219
95.6%

Length

2021-10-30T21:11:22.278133image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
of13
 
3.6%
republic12
 
3.4%
islands12
 
3.4%
and10
 
2.8%
united5
 
1.4%
saint5
 
1.4%
china3
 
0.8%
democratic3
 
0.8%
the3
 
0.8%
states3
 
0.8%
Other values (272)288
80.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Region
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct22
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
Caribbean
25 
EasternAfrica
19 
WesternAsia
18 
SouthernEurope
16 
WesternAfrica
16 
Other values (17)
135 

Length

Max length17
Median length13
Mean length12.22270742
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouthernAsia
2nd rowSouthernEurope
3rd rowNorthernAfrica
4th rowPolynesia
5th rowSouthernEurope

Common Values

ValueCountFrequency (%)
Caribbean25
 
10.9%
EasternAfrica19
 
8.3%
WesternAsia18
 
7.9%
SouthernEurope16
 
7.0%
WesternAfrica16
 
7.0%
SouthAmerica14
 
6.1%
NorthernEurope13
 
5.7%
South-easternAsia11
 
4.8%
EasternEurope10
 
4.4%
SouthernAsia9
 
3.9%
Other values (12)78
34.1%

Length

2021-10-30T21:11:22.478561image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
caribbean25
 
10.9%
easternafrica19
 
8.3%
westernasia18
 
7.9%
southerneurope16
 
7.0%
westernafrica16
 
7.0%
southamerica14
 
6.1%
northerneurope13
 
5.7%
south-easternasia11
 
4.8%
easterneurope10
 
4.4%
polynesia9
 
3.9%
Other values (12)78
34.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Surface area (km2)
Categorical

HIGH CARDINALITY
UNIFORM

Distinct225
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
-99
 
3
180
 
2
457
 
2
142600
 
1
622984
 
1
Other values (220)
220 

Length

Max length8
Median length5
Mean length5.03930131
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique222 ?
Unique (%)96.9%

Sample

1st row652864
2nd row28748
3rd row2381741
4th row199
5th row468

Common Values

ValueCountFrequency (%)
-993
 
1.3%
1802
 
0.9%
4572
 
0.9%
1426001
 
0.4%
6229841
 
0.4%
112951
 
0.4%
5872951
 
0.4%
5491
 
0.4%
1811
 
0.4%
3891
 
0.4%
Other values (215)215
93.9%

Length

2021-10-30T21:11:22.636145image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
993
 
1.3%
4572
 
0.9%
1802
 
0.9%
9473031
 
0.4%
2458571
 
0.4%
15641161
 
0.4%
1736261
 
0.4%
10020001
 
0.4%
16287501
 
0.4%
16761981
 
0.4%
Other values (215)215
93.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Population in thousands (2017)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct218
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32756.79476
Minimum1
Maximum1409517
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2021-10-30T21:11:22.848605image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile19
Q1431
median5448
Q319193
95-th percentile104941.4
Maximum1409517
Range1409516
Interquartile range (IQR)18762

Descriptive statistics

Standard deviation133275.0799
Coefficient of variation (CV)4.068623957
Kurtosis90.00303607
Mean32756.79476
Median Absolute Deviation (MAD)5364
Skewness9.146352204
Sum7501306
Variance1.776224693 × 1010
MonotonicityNot monotonic
2021-10-30T21:11:23.052028image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12
 
0.9%
352
 
0.9%
2762
 
0.9%
28902
 
0.9%
2832
 
0.9%
29302
 
0.9%
1052
 
0.9%
562
 
0.9%
552
 
0.9%
1082
 
0.9%
Other values (208)209
91.3%
ValueCountFrequency (%)
12
0.9%
21
0.4%
31
0.4%
41
0.4%
51
0.4%
61
0.4%
112
0.9%
121
0.4%
151
0.4%
171
0.4%
ValueCountFrequency (%)
14095171
0.4%
13391801
0.4%
3244601
0.4%
2639911
0.4%
2092881
0.4%
1970161
0.4%
1908861
0.4%
1646701
0.4%
1439901
0.4%
1291631
0.4%

Population density (per km2, 2017)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct219
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean462.8248908
Minimum0.1
Maximum25969.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2021-10-30T21:11:23.279420image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile4
Q135.9
median88.1
Q3222.8
95-th percentile1056.04
Maximum25969.8
Range25969.7
Interquartile range (IQR)186.9

Descriptive statistics

Standard deviation2305.384253
Coefficient of variation (CV)4.981115534
Kurtosis92.69038305
Mean462.8248908
Median Absolute Deviation (MAD)65.4
Skewness9.314744997
Sum105986.9
Variance5314796.554
MonotonicityNot monotonic
2021-10-30T21:11:23.474932image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43
 
1.3%
51.82
 
0.9%
232
 
0.9%
214.52
 
0.9%
266.92
 
0.9%
46.82
 
0.9%
135.12
 
0.9%
3.62
 
0.9%
18.22
 
0.9%
17.91
 
0.4%
Other values (209)209
91.3%
ValueCountFrequency (%)
0.11
 
0.4%
0.21
 
0.4%
21
 
0.4%
2.11
 
0.4%
3.11
 
0.4%
3.21
 
0.4%
3.31
 
0.4%
3.41
 
0.4%
3.62
0.9%
43
1.3%
ValueCountFrequency (%)
25969.81
0.4%
20821.61
0.4%
8155.51
0.4%
7014.21
0.4%
3457.11
0.4%
1963.91
0.4%
18001
0.4%
1454.41
0.4%
1346.41
0.4%
12651
0.4%

Sex ratio (m per 100 f, 2017)
Real number (ℝ)

HIGH CORRELATION

Distinct129
Distinct (%)56.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.2021834
Minimum-99
Maximum301.2
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)0.9%
Memory size1.9 KiB
2021-10-30T21:11:23.649462image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile88.62
Q196.4
median99
Q3101.7
95-th percentile111.46
Maximum301.2
Range400.2
Interquartile range (IQR)5.3

Descriptive statistics

Standard deviation28.3278371
Coefficient of variation (CV)0.2827067848
Kurtosis37.33301264
Mean100.2021834
Median Absolute Deviation (MAD)2.6
Skewness-0.1046646211
Sum22946.3
Variance802.4663549
MonotonicityNot monotonic
2021-10-30T21:11:23.820974image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.27
 
3.1%
995
 
2.2%
99.35
 
2.2%
98.25
 
2.2%
96.24
 
1.7%
96.74
 
1.7%
96.94
 
1.7%
974
 
1.7%
99.24
 
1.7%
101.84
 
1.7%
Other values (119)183
79.9%
ValueCountFrequency (%)
-992
0.9%
83.51
0.4%
84.91
0.4%
85.11
0.4%
85.41
0.4%
861
0.4%
86.41
0.4%
86.81
0.4%
871
0.4%
88.21
0.4%
ValueCountFrequency (%)
301.21
0.4%
262.41
0.4%
219.21
0.4%
192.81
0.4%
168.31
0.4%
134.91
0.4%
132.91
0.4%
131.81
0.4%
1241
0.4%
113.31
0.4%

GDP: Gross domestic product (million current US$)
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct209
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean321433.8865
Minimum-99
Maximum18036648
Zeros0
Zeros (%)0.0%
Negative21
Negative (%)9.2%
Memory size1.9 KiB
2021-10-30T21:11:24.031448image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile-99
Q12078
median16251
Q3117955
95-th percentile1287953.2
Maximum18036648
Range18036747
Interquartile range (IQR)115877

Descriptive statistics

Standard deviation1478689.864
Coefficient of variation (CV)4.600292396
Kurtosis103.133328
Mean321433.8865
Median Absolute Deviation (MAD)16350
Skewness9.533959878
Sum73608360
Variance2.186523713 × 1012
MonotonicityNot monotonic
2021-10-30T21:11:24.198994image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-9921
 
9.2%
5121
 
0.4%
1851561
 
0.4%
489531
 
0.4%
64751
 
0.4%
13561
 
0.4%
3341
 
0.4%
1001771
 
0.4%
13631
 
0.4%
2962841
 
0.4%
Other values (199)199
86.9%
ValueCountFrequency (%)
-9921
9.2%
331
 
0.4%
591
 
0.4%
1621
 
0.4%
1831
 
0.4%
1891
 
0.4%
2581
 
0.4%
2941
 
0.4%
3151
 
0.4%
3201
 
0.4%
ValueCountFrequency (%)
180366481
0.4%
111584571
0.4%
43830761
0.4%
33636001
0.4%
28580031
0.4%
24189461
0.4%
21162391
0.4%
18215801
0.4%
17725911
0.4%
15528081
0.4%

GDP growth rate (annual %, const. 2005 prices)
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct91
Distinct (%)39.7%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
-99
21 
4.8
 
7
3.1
 
7
1.6
 
6
3.8
 
6
Other values (86)
182 

Length

Max length5
Median length3
Mean length3.139737991
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)16.2%

Sample

1st row-2.4
2nd row2.6
3rd row3.8
4th row-99
5th row0.8

Common Values

ValueCountFrequency (%)
-9921
 
9.2%
4.87
 
3.1%
3.17
 
3.1%
1.66
 
2.6%
3.86
 
2.6%
4.15
 
2.2%
3.55
 
2.2%
1.25
 
2.2%
2.85
 
2.2%
6.64
 
1.7%
Other values (81)158
69.0%

Length

2021-10-30T21:11:24.392480image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9921
 
9.2%
3.17
 
3.1%
4.87
 
3.1%
3.87
 
3.1%
4.16
 
2.6%
2.46
 
2.6%
1.66
 
2.6%
3.55
 
2.2%
1.25
 
2.2%
1.75
 
2.2%
Other values (65)154
67.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

GDP per capita (current US$)
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct208
Distinct (%)90.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14251.87773
Minimum-99
Maximum169491.8
Zeros0
Zeros (%)0.0%
Negative21
Negative (%)9.2%
Memory size1.9 KiB
2021-10-30T21:11:24.555040image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile-99
Q11207.9
median4836.1
Q316344.1
95-th percentile52785.18
Maximum169491.8
Range169590.8
Interquartile range (IQR)15136.2

Descriptive statistics

Standard deviation23391.26693
Coefficient of variation (CV)1.641276145
Kurtosis16.55488277
Mean14251.87773
Median Absolute Deviation (MAD)4289.7
Skewness3.461517026
Sum3263680
Variance547151368.7
MonotonicityNot monotonic
2021-10-30T21:11:24.706635image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-9921
 
9.2%
1106.42
 
0.9%
14764.51
 
0.4%
2425.41
 
0.4%
1614.21
 
0.4%
8980.91
 
0.4%
623.21
 
0.4%
7960.71
 
0.4%
3903.51
 
0.4%
1442.91
 
0.4%
Other values (198)198
86.5%
ValueCountFrequency (%)
-9921
9.2%
144.51
 
0.4%
244.61
 
0.4%
333.21
 
0.4%
3591
 
0.4%
372.91
 
0.4%
401.81
 
0.4%
455.91
 
0.4%
473.21
 
0.4%
486.21
 
0.4%
ValueCountFrequency (%)
169491.81
0.4%
165870.61
0.4%
100160.81
0.4%
94399.91
0.4%
80831.11
0.4%
78586.41
0.4%
74185.51
0.4%
73653.41
0.4%
621321
0.4%
60513.61
0.4%

Economy: Agriculture (% of GVA)
Categorical

HIGH CARDINALITY

Distinct139
Distinct (%)60.7%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
-99
23 
0.7
 
6
2.3
 
5
1.2
 
4
0.6
 
3
Other values (134)
188 

Length

Max length4
Median length3
Mean length3.371179039
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique93 ?
Unique (%)40.6%

Sample

1st row23.3
2nd row22.4
3rd row12.2
4th row-99
5th row0.5

Common Values

ValueCountFrequency (%)
-9923
 
10.0%
0.76
 
2.6%
2.35
 
2.2%
1.24
 
1.7%
0.63
 
1.3%
1.33
 
1.3%
4.13
 
1.3%
1.43
 
1.3%
8.63
 
1.3%
1.83
 
1.3%
Other values (129)173
75.5%

Length

2021-10-30T21:11:24.908065image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9923
 
10.0%
0.76
 
2.6%
2.35
 
2.2%
1.24
 
1.7%
0.53
 
1.3%
19.03
 
1.3%
2.53
 
1.3%
2.43
 
1.3%
6.63
 
1.3%
0.13
 
1.3%
Other values (129)173
75.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Economy: Industry (% of GVA)
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct170
Distinct (%)74.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.95895197
Minimum-99
Maximum79.9
Zeros0
Zeros (%)0.0%
Negative21
Negative (%)9.2%
Memory size1.9 KiB
2021-10-30T21:11:25.098556image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile-99
Q115.4
median25.5
Q332.8
95-th percentile51.06
Maximum79.9
Range178.9
Interquartile range (IQR)17.4

Descriptive statistics

Standard deviation38.68463101
Coefficient of variation (CV)2.424008236
Kurtosis4.502965278
Mean15.95895197
Median Absolute Deviation (MAD)8.5
Skewness-2.281902549
Sum3654.6
Variance1496.500676
MonotonicityNot monotonic
2021-10-30T21:11:25.263149image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-9921
 
9.2%
32.83
 
1.3%
26.43
 
1.3%
283
 
1.3%
28.33
 
1.3%
34.13
 
1.3%
21.53
 
1.3%
23.32
 
0.9%
13.62
 
0.9%
19.72
 
0.9%
Other values (160)184
80.3%
ValueCountFrequency (%)
-9921
9.2%
41
 
0.4%
5.31
 
0.4%
6.51
 
0.4%
6.61
 
0.4%
7.21
 
0.4%
7.41
 
0.4%
7.51
 
0.4%
7.61
 
0.4%
81
 
0.4%
ValueCountFrequency (%)
79.91
0.4%
73.11
0.4%
701
0.4%
67.11
0.4%
60.21
0.4%
59.91
0.4%
582
0.9%
56.41
0.4%
54.51
0.4%
51.21
0.4%

Economy: Services and other activity (% of GVA)
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct173
Distinct (%)75.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.40873362
Minimum-99
Maximum94
Zeros0
Zeros (%)0.0%
Negative21
Negative (%)9.2%
Memory size1.9 KiB
2021-10-30T21:11:25.475581image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile-99
Q147.3
median59.5
Q370.7
95-th percentile86.44
Maximum94
Range193
Interquartile range (IQR)23.4

Descriptive statistics

Standard deviation48.60372985
Coefficient of variation (CV)1.047297051
Kurtosis4.615188944
Mean46.40873362
Median Absolute Deviation (MAD)11.9
Skewness-2.383702814
Sum10627.6
Variance2362.322555
MonotonicityNot monotonic
2021-10-30T21:11:25.635154image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-9921
 
9.2%
59.73
 
1.3%
54.43
 
1.3%
63.33
 
1.3%
37.43
 
1.3%
70.73
 
1.3%
59.23
 
1.3%
51.83
 
1.3%
72.62
 
0.9%
66.22
 
0.9%
Other values (163)183
79.9%
ValueCountFrequency (%)
-9921
9.2%
14.91
 
0.4%
17.91
 
0.4%
25.31
 
0.4%
25.71
 
0.4%
31.11
 
0.4%
321
 
0.4%
32.21
 
0.4%
32.51
 
0.4%
34.61
 
0.4%
ValueCountFrequency (%)
941
0.4%
93.41
0.4%
92.71
0.4%
92.21
0.4%
891
0.4%
88.61
0.4%
88.21
0.4%
87.81
0.4%
87.71
0.4%
87.61
0.4%

Employment: Agriculture (% of employed)
Categorical

HIGH CARDINALITY

Distinct160
Distinct (%)69.9%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
-99
19 
...
 
16
1.2
 
4
2.0
 
3
3.9
 
3
Other values (155)
184 

Length

Max length4
Median length4
Mean length3.515283843
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique129 ?
Unique (%)56.3%

Sample

1st row61.6
2nd row41.4
3rd row10.8
4th row...
5th row-99

Common Values

ValueCountFrequency (%)
-9919
 
8.3%
...16
 
7.0%
1.24
 
1.7%
2.03
 
1.3%
3.93
 
1.3%
8.73
 
1.3%
3.53
 
1.3%
5.03
 
1.3%
4.12
 
0.9%
0.32
 
0.9%
Other values (150)171
74.7%

Length

2021-10-30T21:11:25.833591image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9919
 
8.3%
16
 
7.0%
1.24
 
1.7%
2.03
 
1.3%
3.93
 
1.3%
8.73
 
1.3%
3.53
 
1.3%
5.03
 
1.3%
75.02
 
0.9%
2.22
 
0.9%
Other values (150)171
74.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Employment: Industry (% of employed)
Categorical

HIGH CARDINALITY

Distinct139
Distinct (%)60.7%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
-99
 
18
...
 
16
24.5
 
4
14.2
 
3
19.2
 
3
Other values (134)
185 

Length

Max length4
Median length4
Mean length3.716157205
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)39.7%

Sample

1st row10.0
2nd row18.3
3rd row34.5
4th row...
5th row-99

Common Values

ValueCountFrequency (%)
-9918
 
7.9%
...16
 
7.0%
24.54
 
1.7%
14.23
 
1.3%
19.23
 
1.3%
23.93
 
1.3%
17.93
 
1.3%
20.73
 
1.3%
19.13
 
1.3%
17.73
 
1.3%
Other values (129)170
74.2%

Length

2021-10-30T21:11:26.002141image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9918
 
7.9%
16
 
7.0%
24.54
 
1.7%
19.13
 
1.3%
11.73
 
1.3%
14.93
 
1.3%
17.73
 
1.3%
20.43
 
1.3%
20.73
 
1.3%
17.93
 
1.3%
Other values (129)170
74.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Employment: Services (% of employed)
Categorical

HIGH CARDINALITY

Distinct165
Distinct (%)72.1%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
-99
 
18
...
 
16
40.3
 
4
61.3
 
3
78.2
 
2
Other values (160)
186 

Length

Max length4
Median length4
Mean length3.847161572
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique134 ?
Unique (%)58.5%

Sample

1st row28.5
2nd row40.3
3rd row54.7
4th row...
5th row-99

Common Values

ValueCountFrequency (%)
-9918
 
7.9%
...16
 
7.0%
40.34
 
1.7%
61.33
 
1.3%
78.22
 
0.9%
69.72
 
0.9%
61.02
 
0.9%
69.42
 
0.9%
66.82
 
0.9%
49.12
 
0.9%
Other values (155)176
76.9%

Length

2021-10-30T21:11:26.819954image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9918
 
7.9%
16
 
7.0%
40.34
 
1.7%
61.33
 
1.3%
81.92
 
0.9%
58.32
 
0.9%
67.52
 
0.9%
61.52
 
0.9%
29.52
 
0.9%
49.42
 
0.9%
Other values (155)176
76.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Unemployment (% of labour force)
Categorical

HIGH CARDINALITY

Distinct129
Distinct (%)56.3%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
...
 
16
-99
 
11
5.6
 
5
6.6
 
5
6.8
 
5
Other values (124)
187 

Length

Max length4
Median length3
Mean length3.270742358
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique84 ?
Unique (%)36.7%

Sample

1st row8.6
2nd row15.8
3rd row11.4
4th row...
5th row-99

Common Values

ValueCountFrequency (%)
...16
 
7.0%
-9911
 
4.8%
5.65
 
2.2%
6.65
 
2.2%
6.85
 
2.2%
5.94
 
1.7%
5.54
 
1.7%
5.84
 
1.7%
2.44
 
1.7%
5.33
 
1.3%
Other values (119)168
73.4%

Length

2021-10-30T21:11:27.005458image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
16
 
7.0%
9911
 
4.8%
5.65
 
2.2%
6.65
 
2.2%
6.85
 
2.2%
5.94
 
1.7%
5.54
 
1.7%
5.84
 
1.7%
2.44
 
1.7%
11.33
 
1.3%
Other values (119)168
73.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct202
Distinct (%)88.2%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
.../...
 
15
-99
 
14
62.5/74.5
 
1
39.1/46.5
 
1
45.5/79.5
 
1
Other values (197)
197 

Length

Max length9
Median length9
Mean length8.502183406
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique200 ?
Unique (%)87.3%

Sample

1st row19.3/83.6
2nd row40.2/61.0
3rd row17.0/70.7
4th row.../...
5th row-99

Common Values

ValueCountFrequency (%)
.../...15
 
6.6%
-9914
 
6.1%
62.5/74.51
 
0.4%
39.1/46.51
 
0.4%
45.5/79.51
 
0.4%
49.3/77.81
 
0.4%
53.1/93.61
 
0.4%
40.2/61.01
 
0.4%
52.6/67.81
 
0.4%
64.1/82.61
 
0.4%
Other values (192)192
83.8%

Length

2021-10-30T21:11:27.174045image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
15
 
6.6%
9914
 
6.1%
40.7/58.31
 
0.4%
61.1/68.31
 
0.4%
46.9/74.31
 
0.4%
58.0/79.81
 
0.4%
54.5/61.71
 
0.4%
29.0/53.31
 
0.4%
62.4/72.41
 
0.4%
41.8/55.91
 
0.4%
Other values (192)192
83.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Agricultural production index (2004-2006=100)
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct84
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.27074236
Minimum-99
Maximum199
Zeros0
Zeros (%)0.0%
Negative18
Negative (%)7.9%
Memory size1.9 KiB
2021-10-30T21:11:27.319647image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile-99
Q198
median111
Q3130
95-th percentile158
Maximum199
Range298
Interquartile range (IQR)32

Descriptive statistics

Standard deviation62.7871374
Coefficient of variation (CV)0.6324838105
Kurtosis5.205730348
Mean99.27074236
Median Absolute Deviation (MAD)15
Skewness-2.35337907
Sum22733
Variance3942.224623
MonotonicityNot monotonic
2021-10-30T21:11:27.477195image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-9918
 
7.9%
1028
 
3.5%
1117
 
3.1%
1127
 
3.1%
1046
 
2.6%
1036
 
2.6%
966
 
2.6%
1206
 
2.6%
1095
 
2.2%
1155
 
2.2%
Other values (74)155
67.7%
ValueCountFrequency (%)
-9918
7.9%
341
 
0.4%
391
 
0.4%
611
 
0.4%
652
 
0.9%
661
 
0.4%
691
 
0.4%
701
 
0.4%
781
 
0.4%
801
 
0.4%
ValueCountFrequency (%)
1991
 
0.4%
1931
 
0.4%
1791
 
0.4%
1761
 
0.4%
1752
0.9%
1681
 
0.4%
1671
 
0.4%
1631
 
0.4%
1602
0.9%
1583
1.3%

Food production index (2004-2006=100)
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct87
Distinct (%)38.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.0436681
Minimum-99
Maximum199
Zeros0
Zeros (%)0.0%
Negative18
Negative (%)7.9%
Memory size1.9 KiB
2021-10-30T21:11:27.681680image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile-99
Q198
median112
Q3130
95-th percentile164
Maximum199
Range298
Interquartile range (IQR)32

Descriptive statistics

Standard deviation63.19898828
Coefficient of variation (CV)0.6317140252
Kurtosis5.125638587
Mean100.0436681
Median Absolute Deviation (MAD)15
Skewness-2.329148697
Sum22910
Variance3994.11212
MonotonicityNot monotonic
2021-10-30T21:11:27.842259image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-9918
 
7.9%
1128
 
3.5%
1157
 
3.1%
1027
 
3.1%
1226
 
2.6%
1196
 
2.6%
1096
 
2.6%
1036
 
2.6%
1205
 
2.2%
1345
 
2.2%
Other values (77)155
67.7%
ValueCountFrequency (%)
-9918
7.9%
341
 
0.4%
391
 
0.4%
611
 
0.4%
651
 
0.4%
661
 
0.4%
681
 
0.4%
691
 
0.4%
701
 
0.4%
781
 
0.4%
ValueCountFrequency (%)
1991
0.4%
1861
0.4%
1802
0.9%
1771
0.4%
1761
0.4%
1741
0.4%
1721
0.4%
1681
0.4%
1671
0.4%
1662
0.9%
Distinct206
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
-99
 
18
~0
 
4
4
 
3
14
 
2
9143
 
1
Other values (201)
201 

Length

Max length7
Median length4
Mean length3.973799127
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique202 ?
Unique (%)88.2%

Sample

1st row1458
2nd row1962
3rd row29992
4th row-99
5th row100

Common Values

ValueCountFrequency (%)
-9918
 
7.9%
~04
 
1.7%
43
 
1.3%
142
 
0.9%
91431
 
0.4%
47851
 
0.4%
605711
 
0.4%
462381
 
0.4%
73211
 
0.4%
21
 
0.4%
Other values (196)196
85.6%

Length

2021-10-30T21:11:28.011805image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9918
 
7.9%
04
 
1.7%
43
 
1.3%
142
 
0.9%
7031
 
0.4%
45761
 
0.4%
1964551
 
0.4%
161
 
0.4%
5165881
 
0.4%
1852351
 
0.4%
Other values (196)196
85.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct210
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
-99
 
18
350
 
2
154
 
2
283009
 
1
420969
 
1
Other values (205)
205 

Length

Max length7
Median length4
Mean length4.205240175
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique207 ?
Unique (%)90.4%

Sample

1st row3568
2nd row4669
3rd row47091
4th row-99
5th row1355

Common Values

ValueCountFrequency (%)
-9918
 
7.9%
3502
 
0.9%
1542
 
0.9%
2830091
 
0.4%
4209691
 
0.4%
29041
 
0.4%
4711
 
0.4%
8331
 
0.4%
5605551
 
0.4%
320581
 
0.4%
Other values (200)200
87.3%

Length

2021-10-30T21:11:28.166388image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9918
 
7.9%
1542
 
0.9%
3502
 
0.9%
251751
 
0.4%
54781
 
0.4%
6471
 
0.4%
16251
 
0.4%
38441
 
0.4%
116971
 
0.4%
232601
 
0.4%
Other values (200)200
87.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct211
Distinct (%)92.1%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
-99
 
18
-1079
 
2
-288
 
1
-4050
 
1
-7253
 
1
Other values (206)
206 

Length

Max length7
Median length5
Mean length4.497816594
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique209 ?
Unique (%)91.3%

Sample

1st row-2110
2nd row-2707
3rd row-17099
4th row-99
5th row-1255

Common Values

ValueCountFrequency (%)
-9918
 
7.9%
-10792
 
0.9%
-2881
 
0.4%
-40501
 
0.4%
-72531
 
0.4%
-1131
 
0.4%
-19051
 
0.4%
-173971
 
0.4%
-4391
 
0.4%
6111
 
0.4%
Other values (201)201
87.8%

Length

2021-10-30T21:11:28.346872image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9918
 
7.9%
10792
 
0.9%
81821
 
0.4%
5021
 
0.4%
6191
 
0.4%
62961
 
0.4%
154131
 
0.4%
11571
 
0.4%
2941
 
0.4%
51221
 
0.4%
Other values (201)201
87.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct178
Distinct (%)77.7%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
-99
42 
...
 
8
-210
 
2
-204
 
2
-72
 
2
Other values (173)
173 

Length

Max length7
Median length4
Mean length4.100436681
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique173 ?
Unique (%)75.5%

Sample

1st row-5121
2nd row-1222
3rd row-27229
4th row-99
5th row-99

Common Values

ValueCountFrequency (%)
-9942
 
18.3%
...8
 
3.5%
-2102
 
0.9%
-2042
 
0.9%
-722
 
0.9%
-361
 
0.4%
-322781
 
0.4%
-5791
 
0.4%
137511
 
0.4%
-13481
 
0.4%
Other values (168)168
73.4%

Length

2021-10-30T21:11:28.499495image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9942
 
18.3%
8
 
3.5%
2102
 
0.9%
2042
 
0.9%
722
 
0.9%
962
 
0.9%
8601
 
0.4%
24921
 
0.4%
2799691
 
0.4%
271
 
0.4%
Other values (167)167
72.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Population growth rate (average annual %)
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct58
Distinct (%)25.3%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
1.2
 
13
0.5
 
12
1.6
 
10
0.4
 
8
2.2
 
8
Other values (53)
178 

Length

Max length5
Median length3
Mean length3.170305677
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)7.4%

Sample

1st row3.2
2nd row-0.1
3rd row2.0
4th row-~0.0
5th row-1.6

Common Values

ValueCountFrequency (%)
1.213
 
5.7%
0.512
 
5.2%
1.610
 
4.4%
0.48
 
3.5%
2.28
 
3.5%
0.38
 
3.5%
0.68
 
3.5%
1.48
 
3.5%
0.27
 
3.1%
1.87
 
3.1%
Other values (48)140
61.1%

Length

2021-10-30T21:11:28.662060image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1.215
 
6.6%
0.514
 
6.1%
0.412
 
5.2%
0.112
 
5.2%
1.611
 
4.8%
0.611
 
4.8%
0.310
 
4.4%
0.210
 
4.4%
1.49
 
3.9%
2.28
 
3.5%
Other values (33)117
51.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Urban population (% of total population)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct195
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.50873362
Minimum0
Maximum100
Zeros2
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2021-10-30T21:11:28.827612image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18.64
Q139.8
median59.9
Q379.6
95-th percentile98.88
Maximum100
Range100
Interquartile range (IQR)39.8

Descriptive statistics

Standard deviation25.20640431
Coefficient of variation (CV)0.4235748733
Kurtosis-0.9485785952
Mean59.50873362
Median Absolute Deviation (MAD)20.1
Skewness-0.1796838891
Sum13627.5
Variance635.3628181
MonotonicityNot monotonic
2021-10-30T21:11:28.995137image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10011
 
4.8%
39.93
 
1.3%
73.42
 
0.9%
59.72
 
0.9%
31.52
 
0.9%
69.52
 
0.9%
54.62
 
0.9%
53.62
 
0.9%
42.52
 
0.9%
55.62
 
0.9%
Other values (185)199
86.9%
ValueCountFrequency (%)
02
0.9%
8.41
0.4%
91
0.4%
12.11
0.4%
131
0.4%
14.31
0.4%
16.11
0.4%
16.31
0.4%
18.41
0.4%
18.51
0.4%
ValueCountFrequency (%)
10011
4.8%
99.21
 
0.4%
98.41
 
0.4%
98.31
 
0.4%
97.91
 
0.4%
95.41
 
0.4%
95.32
 
0.9%
94.51
 
0.4%
94.21
 
0.4%
94.11
 
0.4%

Urban population growth rate (average annual %)
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct64
Distinct (%)27.9%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
0.5
 
9
0.1
 
9
0.8
 
9
0.7
 
8
0.9
 
7
Other values (59)
187 

Length

Max length4
Median length3
Mean length3.104803493
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)5.7%

Sample

1st row4.0
2nd row2.2
3rd row2.8
4th row-0.1
5th row0.1

Common Values

ValueCountFrequency (%)
0.59
 
3.9%
0.19
 
3.9%
0.89
 
3.9%
0.78
 
3.5%
0.97
 
3.1%
1.06
 
2.6%
2.76
 
2.6%
1.36
 
2.6%
1.76
 
2.6%
1.66
 
2.6%
Other values (54)157
68.6%

Length

2021-10-30T21:11:29.204576image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0.115
 
6.6%
0.510
 
4.4%
0.710
 
4.4%
0.310
 
4.4%
0.89
 
3.9%
0.99
 
3.9%
1.46
 
2.6%
0.26
 
2.6%
3.86
 
2.6%
1.66
 
2.6%
Other values (44)142
62.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Fertility rate, total (live births per woman)
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct51
Distinct (%)22.3%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
1.5
 
16
2.0
 
14
2.6
 
13
-99
 
11
1.8
 
10
Other values (46)
165 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)4.4%

Sample

1st row5.3
2nd row1.7
3rd row3.0
4th row2.6
5th row1.2

Common Values

ValueCountFrequency (%)
1.516
 
7.0%
2.014
 
6.1%
2.613
 
5.7%
-9911
 
4.8%
1.810
 
4.4%
2.110
 
4.4%
1.69
 
3.9%
2.48
 
3.5%
1.78
 
3.5%
1.97
 
3.1%
Other values (41)123
53.7%

Length

2021-10-30T21:11:29.344234image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1.516
 
7.0%
2.014
 
6.1%
2.613
 
5.7%
9911
 
4.8%
1.810
 
4.4%
2.110
 
4.4%
1.69
 
3.9%
2.48
 
3.5%
1.78
 
3.5%
1.47
 
3.1%
Other values (41)123
53.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Life expectancy at birth (females/males, years)
Categorical

HIGH CARDINALITY
UNIFORM

Distinct211
Distinct (%)92.1%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
-99
 
12
.../...
 
6
83.7/80.0
 
2
74.9/70.7
 
2
70.7/66.6
 
1
Other values (206)
206 

Length

Max length9
Median length9
Mean length8.633187773
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique207 ?
Unique (%)90.4%

Sample

1st row63.5/61.0
2nd row79.9/75.6
3rd row76.5/74.1
4th row77.8/71.1
5th row-99

Common Values

ValueCountFrequency (%)
-9912
 
5.2%
.../...6
 
2.6%
83.7/80.02
 
0.9%
74.9/70.72
 
0.9%
70.7/66.61
 
0.4%
77.7/66.51
 
0.4%
79.3/73.71
 
0.4%
82.4/78.71
 
0.4%
59.5/56.71
 
0.4%
78.8/74.01
 
0.4%
Other values (201)201
87.8%

Length

2021-10-30T21:11:29.472892image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9912
 
5.2%
6
 
2.6%
83.7/80.02
 
0.9%
74.9/70.72
 
0.9%
64.1/61.21
 
0.4%
73.6/69.41
 
0.4%
83.8/80.61
 
0.4%
74.8/71.11
 
0.4%
77.1/67.91
 
0.4%
84.5/80.11
 
0.4%
Other values (201)201
87.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Population age distribution (0-14 / 60+ years, %)
Categorical

HIGH CARDINALITY
UNIFORM

Distinct221
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
.../...
 
5
-99
 
3
29.0/8.4
 
2
42.3/5.2
 
2
19.8/20.8
 
1
Other values (216)
216 

Length

Max length9
Median length8
Mean length8.406113537
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique217 ?
Unique (%)94.8%

Sample

1st row43.2/4.1
2nd row17.4/19.0
3rd row29.3/9.4
4th row33.3/9.0
5th row14.4/19.0

Common Values

ValueCountFrequency (%)
.../...5
 
2.2%
-993
 
1.3%
29.0/8.42
 
0.9%
42.3/5.22
 
0.9%
19.8/20.81
 
0.4%
31.4/6.41
 
0.4%
31.6/9.51
 
0.4%
14.7/25.31
 
0.4%
44.0/4.51
 
0.4%
35.0/6.41
 
0.4%
Other values (211)211
92.1%

Length

2021-10-30T21:11:29.611522image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5
 
2.2%
993
 
1.3%
29.0/8.42
 
0.9%
42.3/5.22
 
0.9%
19.2/7.61
 
0.4%
40.6/5.31
 
0.4%
15.3/24.91
 
0.4%
39.9/4.61
 
0.4%
23.1/12.01
 
0.4%
20.1/25.31
 
0.4%
Other values (211)211
92.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

International migrant stock (000/% of total pop.)
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct229
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
387.5/2.4
 
1
2043.9/1.6
 
1
12.6/1.6
 
1
491.6/0.5
 
1
34.5/12.1
 
1
Other values (224)
224 

Length

Max length12
Median length9
Mean length9.03930131
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique229 ?
Unique (%)100.0%

Sample

1st row382.4/1.2
2nd row57.6/2.0
3rd row242.4/0.6
4th row23.2/41.8
5th row42.1/59.7

Common Values

ValueCountFrequency (%)
387.5/2.41
 
0.4%
2043.9/1.61
 
0.4%
12.6/1.61
 
0.4%
491.6/0.51
 
0.4%
34.5/12.11
 
0.4%
17.6/0.61
 
0.4%
5.5/37.41
 
0.4%
1.0/15.71
 
0.4%
2543.6/45.41
 
0.4%
545.7/0.71
 
0.4%
Other values (219)219
95.6%

Length

2021-10-30T21:11:29.748154image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
387.5/2.41
 
0.4%
421.7/8.81
 
0.4%
23.2/41.81
 
0.4%
3142.5/5.81
 
0.4%
1845.0/41.11
 
0.4%
2514.2/8.31
 
0.4%
255.5/5.51
 
0.4%
202.3/15.41
 
0.4%
13.8/1.51
 
0.4%
286.8/2.61
 
0.4%
Other values (219)219
95.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct140
Distinct (%)61.1%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
-99
41 
~0.0
25 
...
 
5
0.1
 
4
0.8
 
4
Other values (135)
150 

Length

Max length6
Median length4
Mean length3.864628821
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique121 ?
Unique (%)52.8%

Sample

1st row1513.1
2nd row8.8
3rd row99.8
4th row-99
5th row-99

Common Values

ValueCountFrequency (%)
-9941
 
17.9%
~0.025
 
10.9%
...5
 
2.2%
0.14
 
1.7%
0.84
 
1.7%
0.63
 
1.3%
0.52
 
0.9%
94.72
 
0.9%
0.32
 
0.9%
1.32
 
0.9%
Other values (130)139
60.7%

Length

2021-10-30T21:11:29.888784image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9941
 
17.9%
0.025
 
10.9%
5
 
2.2%
0.14
 
1.7%
0.84
 
1.7%
0.63
 
1.3%
0.42
 
0.9%
8.82
 
0.9%
2.52
 
0.9%
4.72
 
0.9%
Other values (130)139
60.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct157
Distinct (%)68.6%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
-99
23 
3.4
 
5
9.6
 
5
46.9
 
3
3.9
 
3
Other values (152)
190 

Length

Max length4
Median length4
Mean length3.550218341
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique119 ?
Unique (%)52.0%

Sample

1st row68.6
2nd row14.6
3rd row27.7
4th row9.6
5th row-99

Common Values

ValueCountFrequency (%)
-9923
 
10.0%
3.45
 
2.2%
9.65
 
2.2%
46.93
 
1.3%
3.93
 
1.3%
6.53
 
1.3%
3.03
 
1.3%
9.33
 
1.3%
46.53
 
1.3%
3.53
 
1.3%
Other values (147)175
76.4%

Length

2021-10-30T21:11:30.055303image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9923
 
10.0%
9.65
 
2.2%
3.45
 
2.2%
46.93
 
1.3%
3.93
 
1.3%
6.53
 
1.3%
3.03
 
1.3%
9.33
 
1.3%
46.53
 
1.3%
3.53
 
1.3%
Other values (147)175
76.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Health: Total expenditure (% of GDP)
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct90
Distinct (%)39.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-11.2489083
Minimum-99
Maximum17.1
Zeros0
Zeros (%)0.0%
Negative39
Negative (%)17.0%
Memory size1.9 KiB
2021-10-30T21:11:30.209927image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile-99
Q13.4
median5.6
Q37.8
95-th percentile11.1
Maximum17.1
Range116.1
Interquartile range (IQR)4.4

Descriptive statistics

Standard deviation39.92493682
Coefficient of variation (CV)-3.549227691
Kurtosis1.099417831
Mean-11.2489083
Median Absolute Deviation (MAD)2.2
Skewness-1.749152091
Sum-2576
Variance1594.00058
MonotonicityNot monotonic
2021-10-30T21:11:30.373469image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-9939
 
17.0%
7.46
 
2.6%
5.66
 
2.6%
7.55
 
2.2%
5.55
 
2.2%
4.75
 
2.2%
55
 
2.2%
3.65
 
2.2%
6.44
 
1.7%
7.84
 
1.7%
Other values (80)145
63.3%
ValueCountFrequency (%)
-9939
17.0%
1.51
 
0.4%
1.91
 
0.4%
2.11
 
0.4%
2.21
 
0.4%
2.31
 
0.4%
2.62
 
0.9%
2.71
 
0.4%
2.82
 
0.9%
32
 
0.9%
ValueCountFrequency (%)
17.12
0.9%
16.51
0.4%
13.72
0.9%
11.91
0.4%
11.71
0.4%
11.51
0.4%
11.41
0.4%
11.31
0.4%
11.21
0.4%
11.12
0.9%

Health: Physicians (per 1000 pop.)
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct49
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
...
66 
-99
39 
0.2
 
9
2.8
 
6
1.5
 
6
Other values (44)
103 

Length

Max length4
Median length3
Mean length3.017467249
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)6.6%

Sample

1st row0.3
2nd row1.3
3rd row...
4th row-99
5th row3.7

Common Values

ValueCountFrequency (%)
...66
28.8%
-9939
17.0%
0.29
 
3.9%
2.86
 
2.6%
1.56
 
2.6%
1.95
 
2.2%
2.55
 
2.2%
0.15
 
2.2%
3.34
 
1.7%
2.34
 
1.7%
Other values (39)80
34.9%

Length

2021-10-30T21:11:30.537047image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
66
28.8%
9939
17.0%
0.29
 
3.9%
2.86
 
2.6%
1.56
 
2.6%
1.95
 
2.2%
2.55
 
2.2%
0.15
 
2.2%
4.14
 
1.7%
3.44
 
1.7%
Other values (39)80
34.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Education: Government expenditure (% of GDP)
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct63
Distinct (%)27.5%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
-99
45 
...
36 
5.5
 
8
2.8
 
6
5.0
 
6
Other values (58)
128 

Length

Max length4
Median length3
Mean length3.004366812
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)10.0%

Sample

1st row3.3
2nd row3.5
3rd row...
4th row-99
5th row3.3

Common Values

ValueCountFrequency (%)
-9945
19.7%
...36
 
15.7%
5.58
 
3.5%
2.86
 
2.6%
5.06
 
2.6%
5.36
 
2.6%
4.16
 
2.6%
4.95
 
2.2%
3.65
 
2.2%
3.35
 
2.2%
Other values (53)101
44.1%

Length

2021-10-30T21:11:30.666698image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9945
19.7%
36
 
15.7%
5.58
 
3.5%
5.06
 
2.6%
5.36
 
2.6%
4.16
 
2.6%
2.86
 
2.6%
4.95
 
2.2%
3.65
 
2.2%
3.35
 
2.2%
Other values (53)101
44.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct187
Distinct (%)81.7%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
-99
33 
.../...
 
10
103.8/104.1
 
2
89.0/90.5
 
1
107.5/108.6
 
1
Other values (182)
182 

Length

Max length11
Median length11
Mean length9.161572052
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique184 ?
Unique (%)80.3%

Sample

1st row91.1/131.6
2nd row111.7/115.5
3rd row112.7/119.5
4th row-99
5th row-99

Common Values

ValueCountFrequency (%)
-9933
 
14.4%
.../...10
 
4.4%
103.8/104.12
 
0.9%
89.0/90.51
 
0.4%
107.5/108.61
 
0.4%
105.6/104.51
 
0.4%
108.1/108.41
 
0.4%
123.7/134.21
 
0.4%
94.3/94.41
 
0.4%
109.5/113.31
 
0.4%
Other values (177)177
77.3%

Length

2021-10-30T21:11:30.829273image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9933
 
14.4%
10
 
4.4%
103.8/104.12
 
0.9%
112.7/119.51
 
0.4%
111.6/117.71
 
0.4%
99.8/103.81
 
0.4%
121.4/124.21
 
0.4%
108.4/109.31
 
0.4%
114.3/121.01
 
0.4%
89.1/98.51
 
0.4%
Other values (177)177
77.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct176
Distinct (%)76.9%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
-99
37 
.../...
18 
106.1/110.3
 
1
75.5/67.7
 
1
97.6/102.4
 
1
Other values (171)
171 

Length

Max length11
Median length9
Mean length8.35371179
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique174 ?
Unique (%)76.0%

Sample

1st row39.7/70.7
2nd row92.5/98.8
3rd row101.7/98.1
4th row-99
5th row-99

Common Values

ValueCountFrequency (%)
-9937
 
16.2%
.../...18
 
7.9%
106.1/110.31
 
0.4%
75.5/67.71
 
0.4%
97.6/102.41
 
0.4%
39.7/70.71
 
0.4%
99.6/105.61
 
0.4%
99.0/99.41
 
0.4%
113.5/110.61
 
0.4%
95.6/93.21
 
0.4%
Other values (166)166
72.5%

Length

2021-10-30T21:11:31.013741image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9937
 
16.2%
18
 
7.9%
93.0/86.51
 
0.4%
103.5/101.11
 
0.4%
63.3/67.91
 
0.4%
129.4/125.61
 
0.4%
103.6/82.01
 
0.4%
103.8/101.61
 
0.4%
105.0/107.91
 
0.4%
80.4/73.11
 
0.4%
Other values (166)166
72.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct160
Distinct (%)69.9%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
-99
49 
.../...
22 
31.1/15.1
 
1
94.4/83.0
 
1
76.1/49.1
 
1
Other values (155)
155 

Length

Max length11
Median length9
Mean length7.288209607
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique158 ?
Unique (%)69.0%

Sample

1st row3.7/13.3
2nd row68.1/48.7
3rd row45.1/28.9
4th row-99
5th row-99

Common Values

ValueCountFrequency (%)
-9949
 
21.4%
.../...22
 
9.6%
31.1/15.11
 
0.4%
94.4/83.01
 
0.4%
76.1/49.11
 
0.4%
85.4/65.01
 
0.4%
54.4/34.51
 
0.4%
89.2/74.31
 
0.4%
20.7/18.21
 
0.4%
75.5/54.61
 
0.4%
Other values (150)150
65.5%

Length

2021-10-30T21:11:31.192263image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9949
 
21.4%
22
 
9.6%
27.5/28.71
 
0.4%
30.5/27.71
 
0.4%
27.7/19.21
 
0.4%
60.9/65.71
 
0.4%
65.7/58.01
 
0.4%
6.9/9.11
 
0.4%
16.8/15.81
 
0.4%
6.4/14.91
 
0.4%
Other values (150)150
65.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Seats held by women in national parliaments %
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct152
Distinct (%)66.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.861572052
Minimum-99
Maximum61.3
Zeros4
Zeros (%)1.7%
Negative37
Negative (%)16.2%
Memory size1.9 KiB
2021-10-30T21:11:31.348842image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile-99
Q17.2
median17.1
Q327.1
95-th percentile40.62
Maximum61.3
Range160.3
Interquartile range (IQR)19.9

Descriptive statistics

Standard deviation45.67463507
Coefficient of variation (CV)24.53551825
Kurtosis1.035807533
Mean1.861572052
Median Absolute Deviation (MAD)10
Skewness-1.618294696
Sum426.3
Variance2086.172289
MonotonicityNot monotonic
2021-10-30T21:11:31.515434image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-9937
 
16.2%
165
 
2.2%
04
 
1.7%
25.83
 
1.3%
19.23
 
1.3%
16.73
 
1.3%
203
 
1.3%
26.73
 
1.3%
19.92
 
0.9%
172
 
0.9%
Other values (142)164
71.6%
ValueCountFrequency (%)
-9937
16.2%
04
 
1.7%
1.21
 
0.4%
21
 
0.4%
2.61
 
0.4%
2.71
 
0.4%
3.12
 
0.9%
3.81
 
0.4%
4.91
 
0.4%
5.61
 
0.4%
ValueCountFrequency (%)
61.31
0.4%
53.11
0.4%
48.91
0.4%
47.61
0.4%
45.71
0.4%
43.61
0.4%
42.71
0.4%
42.61
0.4%
42.21
0.4%
421
0.4%
Distinct196
Distinct (%)85.6%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
-99
 
13
...
 
7
87.1
 
3
111.5
 
3
114.0
 
2
Other values (191)
201 

Length

Max length5
Median length5
Mean length4.445414847
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique181 ?
Unique (%)79.0%

Sample

1st row61.6
2nd row106.4
3rd row113.0
4th row...
5th row88.1

Common Values

ValueCountFrequency (%)
-9913
 
5.7%
...7
 
3.1%
87.13
 
1.3%
111.53
 
1.3%
114.02
 
0.9%
148.72
 
0.9%
125.82
 
0.9%
105.42
 
0.9%
129.32
 
0.9%
70.52
 
0.9%
Other values (186)191
83.4%

Length

2021-10-30T21:11:31.721877image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9913
 
5.7%
7
 
3.1%
87.13
 
1.3%
111.53
 
1.3%
70.52
 
0.9%
129.52
 
0.9%
132.82
 
0.9%
115.72
 
0.9%
115.22
 
0.9%
143.92
 
0.9%
Other values (186)191
83.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct195
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
-99
 
16
64.6
 
3
19.0
 
3
67.6
 
2
30.0
 
2
Other values (190)
203 

Length

Max length4
Median length4
Mean length3.825327511
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique177 ?
Unique (%)77.3%

Sample

1st row8.3
2nd row63.3
3rd row38.2
4th row-99
5th row96.9

Common Values

ValueCountFrequency (%)
-9916
 
7.0%
64.63
 
1.3%
19.03
 
1.3%
67.62
 
0.9%
30.02
 
0.9%
38.22
 
0.9%
18.02
 
0.9%
77.02
 
0.9%
51.92
 
0.9%
82.12
 
0.9%
Other values (185)193
84.3%

Length

2021-10-30T21:11:31.891423image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9916
 
7.0%
19.03
 
1.3%
64.63
 
1.3%
98.32
 
0.9%
37.62
 
0.9%
73.12
 
0.9%
23.52
 
0.9%
42.82
 
0.9%
9.02
 
0.9%
74.02
 
0.9%
Other values (185)193
84.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Individuals using the Internet (per 100 inhabitants)
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION

Distinct172
Distinct (%)75.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean198.7117904
Minimum-99
Maximum2358
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)0.4%
Memory size1.9 KiB
2021-10-30T21:11:32.045979image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile21.8
Q155
median97
Q3196
95-th percentile814.2
Maximum2358
Range2457
Interquartile range (IQR)141

Descriptive statistics

Standard deviation296.4530892
Coefficient of variation (CV)1.491874683
Kurtosis16.21182423
Mean198.7117904
Median Absolute Deviation (MAD)50
Skewness3.549465771
Sum45505
Variance87884.43411
MonotonicityNot monotonic
2021-10-30T21:11:32.257443image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
494
 
1.7%
544
 
1.7%
1044
 
1.7%
883
 
1.3%
523
 
1.3%
563
 
1.3%
313
 
1.3%
233
 
1.3%
583
 
1.3%
863
 
1.3%
Other values (162)196
85.6%
ValueCountFrequency (%)
-991
 
0.4%
12
0.9%
31
 
0.4%
61
 
0.4%
112
0.9%
121
 
0.4%
131
 
0.4%
181
 
0.4%
212
0.9%
233
1.3%
ValueCountFrequency (%)
23581
0.4%
15131
0.4%
13241
0.4%
12811
0.4%
12721
0.4%
11621
0.4%
10821
0.4%
10801
0.4%
10521
0.4%
9901
0.4%

Threatened species (number)
Categorical

HIGH CARDINALITY
UNIFORM

Distinct194
Distinct (%)84.7%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
-99
 
8
0.0
 
6
0.1
 
3
34.0
 
2
42.3
 
2
Other values (189)
208 

Length

Max length4
Median length4
Mean length3.746724891
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique170 ?
Unique (%)74.2%

Sample

1st row2.1
2nd row28.2
3rd row0.8
4th row87.9
5th row34.0

Common Values

ValueCountFrequency (%)
-998
 
3.5%
0.06
 
2.6%
0.13
 
1.3%
34.02
 
0.9%
42.32
 
0.9%
~0.02
 
0.9%
46.92
 
0.9%
50.82
 
0.9%
1.12
 
0.9%
0.22
 
0.9%
Other values (184)198
86.5%

Length

2021-10-30T21:11:32.420012image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
998
 
3.5%
0.08
 
3.5%
0.13
 
1.3%
40.32
 
0.9%
34.02
 
0.9%
3.82
 
0.9%
8.12
 
0.9%
12.32
 
0.9%
11.02
 
0.9%
6.62
 
0.9%
Other values (183)196
85.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Forested area (% of land area)
Categorical

HIGH CARDINALITY
UNIFORM

Distinct214
Distinct (%)93.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
-99
 
13
0.3/0.1
 
2
0.1/0.6
 
2
0.1/1.9
 
2
34.8/3.0
 
1
Other values (209)
209 

Length

Max length11
Median length8
Mean length7.537117904
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique210 ?
Unique (%)91.7%

Sample

1st row9.8/0.3
2nd row5.7/2.0
3rd row145.4/3.7
4th row-99
5th row0.5/6.4

Common Values

ValueCountFrequency (%)
-9913
 
5.7%
0.3/0.12
 
0.9%
0.1/0.62
 
0.9%
0.1/1.92
 
0.9%
34.8/3.01
 
0.4%
0.2/6.11
 
0.4%
242.8/8.11
 
0.4%
1705.3/11.91
 
0.4%
529.8/2.61
 
0.4%
9.6/1.71
 
0.4%
Other values (204)204
89.1%

Length

2021-10-30T21:11:32.579585image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9913
 
5.7%
0.1/1.92
 
0.9%
0.3/0.12
 
0.9%
0.1/0.62
 
0.9%
0.5/1.41
 
0.4%
0.0/4.81
 
0.4%
0.1/9.81
 
0.4%
6.1/5.21
 
0.4%
21.5/2.11
 
0.4%
3.1/0.11
 
0.4%
Other values (204)204
89.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CO2 emission estimates (million tons/tons per capita)
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct155
Distinct (%)67.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2464.262009
Minimum-99
Maximum101394
Zeros23
Zeros (%)10.0%
Negative20
Negative (%)8.7%
Memory size1.9 KiB
2021-10-30T21:11:32.740154image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile-99
Q12
median108
Q3961
95-th percentile8956.4
Maximum101394
Range101493
Interquartile range (IQR)959

Descriptive statistics

Standard deviation9912.891745
Coefficient of variation (CV)4.022661434
Kurtosis65.96304017
Mean2464.262009
Median Absolute Deviation (MAD)144
Skewness7.662185624
Sum564316
Variance98265422.76
MonotonicityNot monotonic
2021-10-30T21:11:32.932646image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
023
 
10.0%
-9920
 
8.7%
112
 
5.2%
25
 
2.2%
34
 
1.7%
74
 
1.7%
843
 
1.3%
313
 
1.3%
192
 
0.9%
82
 
0.9%
Other values (145)151
65.9%
ValueCountFrequency (%)
-9920
8.7%
023
10.0%
112
5.2%
25
 
2.2%
34
 
1.7%
52
 
0.9%
61
 
0.4%
74
 
1.7%
82
 
0.9%
92
 
0.9%
ValueCountFrequency (%)
1013941
0.4%
838871
0.4%
548291
0.4%
259041
0.4%
231031
0.4%
194811
0.4%
192761
0.4%
152821
0.4%
132911
0.4%
109481
0.4%

Energy production, primary (Petajoules)
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct121
Distinct (%)52.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.84716157
Minimum-99
Maximum952
Zeros0
Zeros (%)0.0%
Negative10
Negative (%)4.4%
Memory size1.9 KiB
2021-10-30T21:11:33.114161image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile3.4
Q119
median47
Q3103
95-th percentile275.8
Maximum952
Range1051
Interquartile range (IQR)84

Descriptive statistics

Standard deviation121.8082627
Coefficient of variation (CV)1.506648598
Kurtosis18.63050563
Mean80.84716157
Median Absolute Deviation (MAD)35
Skewness3.399389597
Sum18514
Variance14837.25285
MonotonicityNot monotonic
2021-10-30T21:11:33.319575image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-9910
 
4.4%
126
 
2.6%
626
 
2.6%
206
 
2.6%
175
 
2.2%
144
 
1.7%
854
 
1.7%
284
 
1.7%
194
 
1.7%
164
 
1.7%
Other values (111)176
76.9%
ValueCountFrequency (%)
-9910
4.4%
21
 
0.4%
31
 
0.4%
41
 
0.4%
53
 
1.3%
64
 
1.7%
74
 
1.7%
82
 
0.9%
91
 
0.4%
114
 
1.7%
ValueCountFrequency (%)
9521
0.4%
8461
0.4%
6071
0.4%
4131
0.4%
3801
0.4%
3661
0.4%
3571
0.4%
3241
0.4%
3031
0.4%
2901
0.4%

Energy supply per capita (Gigajoules)
Categorical

HIGH CARDINALITY

Distinct163
Distinct (%)71.2%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
100.0/100.0
38 
-99
20 
.../...
 
5
100.0/99.0
 
3
99.0/99.0
 
3
Other values (158)
160 

Length

Max length11
Median length9
Mean length8.812227074
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique156 ?
Unique (%)68.1%

Sample

1st row78.2/47.0
2nd row94.9/95.2
3rd row84.3/81.8
4th row100.0/100.0
5th row100.0/100.0

Common Values

ValueCountFrequency (%)
100.0/100.038
 
16.6%
-9920
 
8.7%
.../...5
 
2.2%
100.0/99.03
 
1.3%
99.0/99.03
 
1.3%
95.1/95.12
 
0.9%
100.0/...2
 
0.9%
94.6/77.01
 
0.4%
98.9/100.01
 
0.4%
90.9/91.81
 
0.4%
Other values (153)153
66.8%

Length

2021-10-30T21:11:33.492144image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
100.0/100.038
 
16.6%
9920
 
8.7%
5
 
2.2%
100.0/99.03
 
1.3%
99.0/99.03
 
1.3%
100.03
 
1.3%
95.1/95.12
 
0.9%
91.4/44.21
 
0.4%
89.6/54.41
 
0.4%
98.7/65.31
 
0.4%
Other values (152)152
66.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct186
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
-99
21 
100.0/100.0
 
16
.../...
 
6
100.0/...
 
2
97.5/98.3
 
2
Other values (181)
182 

Length

Max length11
Median length9
Mean length8.462882096
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique180 ?
Unique (%)78.6%

Sample

1st row45.1/27.0
2nd row95.5/90.2
3rd row89.8/82.2
4th row62.5/62.5
5th row100.0/100.0

Common Values

ValueCountFrequency (%)
-9921
 
9.2%
100.0/100.016
 
7.0%
.../...6
 
2.6%
100.0/...2
 
0.9%
97.5/98.32
 
0.9%
98.6/98.92
 
0.9%
96.1/95.91
 
0.4%
92.2/63.31
 
0.4%
43.8/48.61
 
0.4%
99.8/100.01
 
0.4%
Other values (176)176
76.9%

Length

2021-10-30T21:11:33.661692image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9921
 
9.2%
100.0/100.016
 
7.0%
6
 
2.6%
100.02
 
0.9%
97.5/98.32
 
0.9%
98.6/98.92
 
0.9%
77.9/70.81
 
0.4%
99.9/99.81
 
0.4%
92.5/34.11
 
0.4%
33.5/8.51
 
0.4%
Other values (176)176
76.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct120
Distinct (%)52.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
-99
87 
...
 
9
0.02
 
4
0.00
 
3
0.22
 
2
Other values (115)
124 

Length

Max length5
Median length4
Mean length3.689956332
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique106 ?
Unique (%)46.3%

Sample

1st row21.43
2nd row2.96
3rd row0.05
4th row-99
5th row-99

Common Values

ValueCountFrequency (%)
-9987
38.0%
...9
 
3.9%
0.024
 
1.7%
0.003
 
1.3%
0.222
 
0.9%
3.932
 
0.9%
0.472
 
0.9%
0.012
 
0.9%
0.052
 
0.9%
0.422
 
0.9%
Other values (110)114
49.8%

Length

2021-10-30T21:11:33.826250image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9987
38.0%
9
 
3.9%
0.024
 
1.7%
0.003
 
1.3%
0.422
 
0.9%
0.662
 
0.9%
1.242
 
0.9%
0.152
 
0.9%
0.972
 
0.9%
0.052
 
0.9%
Other values (110)114
49.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Net Official Development Assist. received (% of GNI)
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
-99
229 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-99
2nd row-99
3rd row-99
4th row-99
5th row-99

Common Values

ValueCountFrequency (%)
-99229
100.0%

Length

2021-10-30T21:11:33.968837image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-30T21:11:34.056604image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
99229
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2021-10-30T21:11:13.949878image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:10:31.949069image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:10:35.259615image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:10:38.029247image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:10:40.919482image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:10:44.058640image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:10:47.098513image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:10:49.916010image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:10:52.904849image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:10:57.462931image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:11:00.270527image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:11:02.838589image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:11:05.566265image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:11:08.503412image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:11:11.191224image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:11:14.165315image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:10:32.217749image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:10:35.440133image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:10:38.218704image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:10:41.116968image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:10:44.276061image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:10:47.289015image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:10:50.097493image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:10:53.078749image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:10:57.711268image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:11:00.437975image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:11:03.048995image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:11:05.796647image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:11:08.702884image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:11:11.392118image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:11:14.363787image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:10:32.449130image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:10:35.636609image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:10:38.398223image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:10:41.328387image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:10:44.448599image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:10:47.454594image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:10:50.308928image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:10:53.236339image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:10:57.882807image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:11:00.605527image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:11:03.211591image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:11:05.992124image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:11:08.856464image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:11:11.613094image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:11:14.545301image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:10:32.635741image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:10:35.824107image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:10:38.624618image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:10:41.514889image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:10:44.721867image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:10:47.664003image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:10:50.493434image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:10:53.440771image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:10:58.054427image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:11:00.771085image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:11:03.449921image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:11:06.197608image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:11:09.094827image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:11:11.781643image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T21:11:14.790674image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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Correlations

2021-10-30T21:11:34.167308image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-30T21:11:34.728807image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-30T21:11:35.304300image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-30T21:11:35.821915image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-10-30T21:11:36.177932image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-30T21:11:17.021712image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-10-30T21:11:21.344119image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

countryRegionSurface area (km2)Population in thousands (2017)Population density (per km2, 2017)Sex ratio (m per 100 f, 2017)GDP: Gross domestic product (million current US$)GDP growth rate (annual %, const. 2005 prices)GDP per capita (current US$)Economy: Agriculture (% of GVA)Economy: Industry (% of GVA)Economy: Services and other activity (% of GVA)Employment: Agriculture (% of employed)Employment: Industry (% of employed)Employment: Services (% of employed)Unemployment (% of labour force)Labour force participation (female/male pop. %)Agricultural production index (2004-2006=100)Food production index (2004-2006=100)International trade: Exports (million US$)International trade: Imports (million US$)International trade: Balance (million US$)Balance of payments, current account (million US$)Population growth rate (average annual %)Urban population (% of total population)Urban population growth rate (average annual %)Fertility rate, total (live births per woman)Life expectancy at birth (females/males, years)Population age distribution (0-14 / 60+ years, %)International migrant stock (000/% of total pop.)Refugees and others of concern to UNHCR (in thousands)Infant mortality rate (per 1000 live birthsHealth: Total expenditure (% of GDP)Health: Physicians (per 1000 pop.)Education: Government expenditure (% of GDP)Education: Primary gross enrol. ratio (f/m per 100 pop.)Education: Secondary gross enrol. ratio (f/m per 100 pop.)Education: Tertiary gross enrol. ratio (f/m per 100 pop.)Seats held by women in national parliaments %Mobile-cellular subscriptions (per 100 inhabitants)Mobile-cellular subscriptions (per 100 inhabitants).1Individuals using the Internet (per 100 inhabitants)Threatened species (number)Forested area (% of land area)CO2 emission estimates (million tons/tons per capita)Energy production, primary (Petajoules)Energy supply per capita (Gigajoules)Pop. using improved drinking water (urban/rural, %)Pop. using improved sanitation facilities (urban/rural, %)Net Official Development Assist. received (% of GNI)
0AfghanistanSouthernAsia6528643553054.4106.320270-2.4623.223.323.353.361.610.028.58.619.3/83.612512514583568-2110-51213.226.74.05.363.5/61.043.2/4.1382.4/1.21513.168.68.20.33.391.1/131.639.7/70.73.7/13.327.761.68.3422.19.8/0.363578.2/47.045.1/27.021.43-99
1AlbaniaSouthernEurope287482930106.9101.9115412.63984.222.426.051.741.418.340.315.840.2/61.013413419624669-2707-1222-0.157.42.21.779.9/75.617.4/19.057.6/2.08.814.65.91.33.5111.7/115.592.5/98.868.1/48.722.9106.463.313028.25.7/2.0843694.9/95.295.5/90.22.96-99
2AlgeriaNorthernAfrica23817414131817.3102.01647793.84154.112.237.350.510.834.554.711.417.0/70.71601612999247091-17099-272292.070.72.83.076.5/74.129.3/9.4242.4/0.699.827.77.2......112.7/119.5101.7/98.145.1/28.931.6113.038.21350.8145.4/3.759005584.3/81.889.8/82.20.05-99
3American SamoaPolynesia19956278.2103.6-99-99-99.0-99-99.0-99.0.............../...112112-99-99-99-99-~0.087.2-0.12.677.8/71.133.3/9.023.2/41.8-999.6-99.0-99-99-99-99-99-99.0...-999287.9-99-99-99100.0/100.062.5/62.5-99-99
4AndorraSouthernEurope46877163.8102.328120.839896.40.510.888.6-99-99-99-99-99-99-991001355-1255-99-1.685.10.11.2-9914.4/19.042.1/59.7-99-998.13.73.3-99-99-9932.188.196.91334.00.5/6.41119100.0/100.0100.0/100.0-99-99
5AngolaMiddleAfrica12467002978423.996.21179553.04714.16.851.242.04.237.658.26.659.8/77.117517621011879012221-102733.544.15.06.063.0/57.446.8/4.0106.8/0.445.765.43.3......100.4/156.922.7/35.18.2/10.438.260.812.414646.534.8/1.439022575.4/28.288.6/22.50.42-99
6AnguillaCaribbean9115165.797.63202.921879.62.315.782.0.............../...-99-992154-153-481.2100.01.2....../...23.3/7.65.5/37.4~0.0-99-99.0-99...-99-99-99-99.0177.976.05261.10.1/9.8013694.6/...97.9/...-99-99
7Antigua and BarbudaCaribbean442102231.892.313564.114764.51.918.379.8.............../...888861491-429-2041.123.8-0.92.178.2/73.323.9/10.928.1/30.6~0.09.15.5-99...94.1/100.0103.8/101.631.1/15.111.1137.265.25522.30.5/5.8-998497.9/97.991.4/91.40.12-99
8ArgentinaSouthAmerica27804004427116.295.96323432.414564.56.027.866.22.024.873.16.548.6/74.411911957733556102124-159441.091.81.02.379.8/72.224.9/15.42086.3/4.85.013.74.83.85.3109.8/110.2110.3/103.4102.9/63.538.9143.969.425610.0204.0/4.731678599.0/100.096.2/98.30.01-99
9ArmeniaWesternAsia297432930102.988.8105293.03489.119.028.352.835.015.749.316.655.3/74.213513517763230-1455-2790.362.7-0.11.677.0/70.620.0/16.9191.2/6.319.313.24.52.82.898.5/98.589.0/88.146.9/41.69.9115.258.211411.75.5/1.84846100.0/100.096.2/78.23.17-99

Last rows

countryRegionSurface area (km2)Population in thousands (2017)Population density (per km2, 2017)Sex ratio (m per 100 f, 2017)GDP: Gross domestic product (million current US$)GDP growth rate (annual %, const. 2005 prices)GDP per capita (current US$)Economy: Agriculture (% of GVA)Economy: Industry (% of GVA)Economy: Services and other activity (% of GVA)Employment: Agriculture (% of employed)Employment: Industry (% of employed)Employment: Services (% of employed)Unemployment (% of labour force)Labour force participation (female/male pop. %)Agricultural production index (2004-2006=100)Food production index (2004-2006=100)International trade: Exports (million US$)International trade: Imports (million US$)International trade: Balance (million US$)Balance of payments, current account (million US$)Population growth rate (average annual %)Urban population (% of total population)Urban population growth rate (average annual %)Fertility rate, total (live births per woman)Life expectancy at birth (females/males, years)Population age distribution (0-14 / 60+ years, %)International migrant stock (000/% of total pop.)Refugees and others of concern to UNHCR (in thousands)Infant mortality rate (per 1000 live birthsHealth: Total expenditure (% of GDP)Health: Physicians (per 1000 pop.)Education: Government expenditure (% of GDP)Education: Primary gross enrol. ratio (f/m per 100 pop.)Education: Secondary gross enrol. ratio (f/m per 100 pop.)Education: Tertiary gross enrol. ratio (f/m per 100 pop.)Seats held by women in national parliaments %Mobile-cellular subscriptions (per 100 inhabitants)Mobile-cellular subscriptions (per 100 inhabitants).1Individuals using the Internet (per 100 inhabitants)Threatened species (number)Forested area (% of land area)CO2 emission estimates (million tons/tons per capita)Energy production, primary (Petajoules)Energy supply per capita (Gigajoules)Pop. using improved drinking water (urban/rural, %)Pop. using improved sanitation facilities (urban/rural, %)Net Official Development Assist. received (% of GNI)
219UruguaySouthAmerica173626345719.893.5534421.015573.86.828.065.28.720.570.88.855.6/76.312913069648137-1173-11410.395.30.52.080.4/73.221.1/19.571.8/2.10.512.78.6...4.4107.3/109.8100.0/90.3.../...20.2160.264.610610.46.7/2.011358100.0/93.996.6/92.60.04-99
220UzbekistanCentralAsia4489693191175.099.4690046.82308.319.232.947.929.023.947.18.948.5/76.615817427947256522295-991.636.41.42.473.5/68.128.0/7.61170.9/3.986.731.35.82.5-99103.2/105.695.1/96.87.1/11.016.073.342.8597.6105.2/3.623396298.5/80.9100.0/100.00.66-99
221VanuatuMelanesia1218927622.7102.4737-1.02783.026.78.464.961.46.831.85.361.7/80.512312250416-366-822.326.13.43.473.6/69.435.9/6.73.2/1.2~0.024.35.00.25.5118.7/120.656.4/53.4.../...0.066.222.413736.10.2/0.611298.9/92.965.1/55.412.32-99
222Venezuela (Bolivarian Republic of)SouthAmerica9120503197736.399.0344331-6.211068.95.344.750.011.926.861.36.651.5/78.311811919731163243407-203601.489.01.52.478.2/69.927.6/9.91404.4/4.5174.213.85.3......98.6/101.393.0/86.5.../...22.293.061.932853.1185.2/6.174609095.0/77.997.5/69.90.01-99
223Viet NamSouth-easternAsia33096795541308.198.01932416.72067.918.937.044.241.822.935.22.273.9/83.313613417663217411125209061.133.63.02.080.3/70.723.1/11.172.8/0.111.019.37.11.25.7108.4/109.3-9928.9/28.826.7130.652.761647.2166.9/1.829773099.1/96.994.4/69.71.73-99
224Wallis and Futuna IslandsPolynesia1421284.193.4-99-99-99.0-99-99.0-99.0-99-99-99-99-99115115153-51-99-2.10.00.02.178.7/72.825.5/15.42.8/21.7-99...-99.0-99-99-99-99-99-99.0-999.08941.6~0.0/1.6-9926-99-99-99-99
225Western SaharaNorthernAfrica2660005532.1110.1-99-99-99.0-99-99.0-99.037.427.934.76.828.6/83.3100100-99-99-99-991.880.93.32.670.3/66.928.1/5.45.2/0.9-9934.1-99.0-99-99-99-99-99-99.0-99-99492.7-99-99-99-99-99-99-99
226YemenWesternAsia5279682825053.5102.129688-28.11106.414.736.948.432.917.949.216.126.2/73.71361375706861-6291-30262.634.64.04.465.6/62.839.9/4.6344.1/1.33371.447.25.60.3...88.9/105.739.5/57.46.1/13.70.068.025.12981.022.7/0.96681272.0/46.592.5/34.12.99-99
227ZambiaEasternAfrica7526121709423.098.5212552.91311.18.232.359.554.89.935.37.469.9/80.917918065057442-937-7683.040.94.35.261.9/57.544.8/3.7127.9/0.855.353.85.00.2...104.0/103.3-993.4/4.518.074.521.08865.64.5/0.33742685.6/51.355.6/35.73.96-99
228ZimbabweEasternAfrica3907571653042.795.0138931.1890.413.030.556.567.57.325.25.078.0/87.5999828325212-2379-15212.332.42.34.059.0/56.141.2/4.2398.9/2.6308.646.56.00.18.499.1/100.847.1/48.18.0/8.932.684.816.48937.212.0/0.84823097.0/67.349.3/30.86.00-99